{"id":"W4404479833","doi":"10.1109/tvt.2024.3499962","title":"Adaptive Prioritization and Task Offloading in Vehicular Edge Computing Through Deep Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Prioritization; Computer science; Task (project management); Edge computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Computer network; Distributed computing; Artificial intelligence; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003861096,0.0002941274,0.0003043279,0.001028211,0.0003146189,0.000179087,0.003721431,0.00048397,0.000005809345],"category_scores_gemma":[0.0003804692,0.0003137286,0.00007341125,0.002173833,0.0002319018,0.0007776438,0.0007628731,0.001384221,0.00003738581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979147,"about_ca_system_score_gemma":0.00006313138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000395514,"about_ca_topic_score_gemma":0.00002257732,"domain_scores_codex":[0.9977152,0.00009148747,0.0004350964,0.0009309505,0.0002967985,0.0005304245],"domain_scores_gemma":[0.9975852,0.0001695439,0.00008283882,0.002053641,0.00006690025,0.0000418895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002733783,0.0002041176,0.0001856937,0.0002633016,0.0003088184,0.00083634,0.001464469,0.4037738,0.01885829,0.04327627,0.0005443363,0.5302573],"study_design_scores_gemma":[0.0003107951,0.0002181172,0.00002266835,0.0003452712,0.0000210715,0.0001149122,0.0001941588,0.9428651,0.02777215,0.02677445,0.001052053,0.0003093025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02736895,0.001330604,0.9617518,0.005811474,0.000474495,0.0003808895,0.000001836084,0.002762841,0.0001171489],"genre_scores_gemma":[0.9124677,0.0003860228,0.08695143,0.00007489085,0.00001626503,0.00005258913,0.000003799998,0.00002867819,0.0000185821],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8850988,"threshold_uncertainty_score":0.9999315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747525296420209,"score_gpt":0.2592201785151448,"score_spread":0.2417449255509427,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}